EcoService Models Library (ESML)
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Compare EMs
Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
EM Identity and Description
EM ID
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EM-12 ![]() |
EM-85 | EM-91 | EM-97 |
EM-98 ![]() |
EM-120 | EM-184 |
EM-321 ![]() |
EM-337 | EM-423 | EM-447 | EM-448 | EM-456 | EM-650 |
EM-719 ![]() |
EM-844 | EM-888 | EM-963 | EM-993 | EM-1022 |
EM Short Name
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Evoland v3.5 (bounded growth), Eugene, OR, USA | Area and hotspots of flow regulation, South Africa | RHyME2, Upper Mississippi River basin, USA | AnnAGNPS, Kaskaskia River watershed, IL, USA | PATCH, western USA | Landscape importance for habitat diversity, Europe | ROS (Recreation Opportunity Spectrum), Europe | Erosion prevention by vegetation, Portel, Portugal | Rate of Fire Spread | Air pollutant removal, Guánica Bay, Puerto Rico | Wave height attenuation, St. Croix, USVI | Wave energy attenuation, St. Croix, USVI | Reef dive site favorability, St. Croix, USVI | Sedge Wren density, CREP, Iowa, USA | Seed mix for native plant establishment, IA, USA | Common yellowthroat abun, Piedmont region, USA | HWB-home value, Great Lakes, USA | Eastern Meadowlark Abundance | Velma- 6PPD-Q concentrations, Seattle, WA | NU-WRF, USA |
EM Full Name
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Evoland v3.5 (with urban growth boundaries), Eugene, OR, USA | Area and hotspots of water flow regulation, South Africa | RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA | AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | PATCH (Program to Assist in Tracking Critical Habitat), western USA | Landscape importance for habitat diversity, Europe | ROS (Recreation Opportunity Spectrum), Europe | Soil erosion prevention provided by vegetation cover, Portel municipality, Portugal | Rate of Fire Spread | Air pollutant removal, Guánica Bay, Puerto Rico, USA | Wave height attenuation (by reef), St. Croix, USVI | Wave energy attenuation (by reef), St. Croix, USVI | Dive site favorability (reef), St. Croix, USVI | Sedge Wren population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Cost-effective seed mix design for native plant establishment, Iowa, USA | Common yellowthroat abundance, Piedmont ecoregion, USA | Human well being indicator-home value, Great Lakes waterfront, USA | TEST: CRP Impacts on Eastern Meadowlark Abundance | VELMA: 6PPD-Quinone stormwater concentrations , Seattle, Washington | NASA Unified Weather Research and Forecasting model |
EM Source or Collection
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Envision | None | US EPA | US EPA | US EPA | EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | None | US EPA | US EPA | US EPA | US EPA | None | None | None | None |
None ?Comment:Could not find any information pertaining to a model collection. |
US EPA | None |
EM Source Document ID
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47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
271 | 123 | 137 | 2 | 228 | 293 | 281 | 306 |
338 ?Comment:Manuscript in revision, should be published by end of 2016. |
335 | 335 | 335 | 372 | 394 | 405 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
405 | 465 | 484 |
Document Author
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Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Tran, L. T., O’Neill, R. V., Smith, E. R., Bruins, R. J. F. and Harden, C. | Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M. | Carroll, C, Phillips, M. K. , Lopez-Gonzales, C. A and Schumaker, N. H. | Haines-Young, R., Potschin, M. and Kienast, F. | Paracchini, M.L., Zulian, G., Kopperoinen, L., Maes, J., Schägner, J.P., Termansen, M., Zandersen, M., Perez-Soba, M., Scholefield, P.A., and Bidoglio, G. | Guerra, C.A., Pinto-Correia, T., Metzger, M.J. | Rothermel, Richard C. | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Meissen, J. | Riffel, S., Scognamillo, D., and L. W. Burger | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | Riffel, S., Scognamillo, D., and L. W. Burger | Halama JJ, McKane RB, Barnhart BL, Pettus PP, Brookes AF, Adams AK, Gockel CK, Djang KS, Phan V, Chokshi SM, Graham JJ, Tian Z, Peter KT and Kolodziej,EP | Peters-Lidard, Christa et al. Sujay V. Kumar, , Jossy P. Jacob b , Thomas Clune f , Wei-Kuo Tao g , Mian Chin h , Arthur Hou I , Jonathan L. Case j , Dongchul Kim k , Kyu-Myong Kim l , William Lau m, Yuqiong Liu n , Jainn Shi o , David Starr g , Qian Tan h , Zhining Tao k , Benjamin F. Zaitchik p , Bradley Zavodsky q , Sara Q. Zhang r , Milija Zupanski |
Document Year
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2008 | 2008 | 2013 | 2011 | 2006 | 2012 | 2014 | 2014 | 1972 | 2017 | 2014 | 2014 | 2014 | 2010 | 2018 | 2008 | None | 2008 | 2024 | 2015 |
Document Title
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Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Mapping ecosystem services for planning and management | Application of hierarchy theory to cross-scale hydrologic modeling of nutrient loads | AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study | Defining recovery goals and strategies for endangered species: The wolf as a case study | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Mapping cultural ecosystem services: A framework to assess the potential for outdoor recreation across the EU | Mapping soil erosion prevention using an ecosystem service modeling framework for integrated land management and policy | A Mathematical model for predicting fire spread in wildland fuels | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Cost-effective seed mix design and first-year management | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Human well-being and natural capital indictors for Great Lakes waterfront revitalization | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Watershed analysis of urban stormwater contaminant 6PPD-Quinone hotspots and stream concentrations using a process-based ecohydrological model | Integrated modeling of aerosol, cloud, precipitation and land processes at satellite-resolved scales |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Documented, not peer reviewed | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed but unpublished (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published USDA Forest Service report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published report | Published journal manuscript | Journal manuscript submitted or in review | Published journal manuscript | Published journal manuscript | Published journal manuscript |
EM ID
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EM-12 ![]() |
EM-85 | EM-91 | EM-97 |
EM-98 ![]() |
EM-120 | EM-184 |
EM-321 ![]() |
EM-337 | EM-423 | EM-447 | EM-448 | EM-456 | EM-650 |
EM-719 ![]() |
EM-844 | EM-888 | EM-963 | EM-993 | EM-1022 |
http://evoland.bioe.orst.edu/ ?Comment:Software is likely available. |
Not applicable | Not applicable | https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/ | Not applicable | Not applicable | Not applicable | Not applicable | http://firelab.org/project/farsite | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not reported | https://git.smce.nasa.gov/astg/nuwrf/nu-wrf-dev | |
Contact Name
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Michael R. Guzy | Benis Egoh | Liem Tran | Yongping Yuan | Carlos Carroll | Marion Potschin | Maria Luisa Paracchini | Carlos A. Guerra | Charles McHugh | Susan H. Yee | Susan H. Yee | Susan H. Yee | Susan H. Yee | David Otis | Justin Meissen | Sam Riffell | Ted Angradi |
L. Wes Burger ?Comment:Lead author, Sam Riffell, pass away. Using last author. |
Jonathan Halama | Christa Peters-Lidard |
Contact Address
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Oregon State University, Dept. of Biological and Ecological Engineering | Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | Department of Geography, University of Tennessee, 1000 Phillip Fulmer Way, Knoxville, TN 37996-0925, USA | U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA | Klamath Center for Conservation Research, Orleans, CA 95556 | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Joint Research Centre, Institute for Environment and Sustainability, Via E.Fermi, 2749, I-21027 Ispra (VA), Italy | Instituto de Ciências Agrárias e Ambientais Mediterrânicas, Universidade de Évora, Pólo da Mitra, Apartado 94, 7002-554 Évora, Portugal | RMRS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, MT 59808 | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Tallgrass Prairie Center, University of Northern Iowa | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 | Mississippi State University, Mississippi State, MS | U.S. Environmental Protection Agency, Corvallis, OR | Hydrospheric and Biospheric Sciences Division, Code 610HB, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA |
Contact Email
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Not reported | Not reported | ltran1@utk.edu | yuan.yongping@epa.gov | carlos@cklamathconservation.org | marion.potschin@nottingham.ac.uk | luisa.paracchini@jrc.ec.europa.eu | cguerra@uevora.pt | cmchugh@fs.fed.us | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | dotis@iastate.edu | Not reported | sriffell@cfr.msstate.edu | tedangradi@gmail.com | w.burger@msstate.edu | Halama.Jonathan@epa.gov | christa.peters@nasa.gov |
EM ID
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EM-12 ![]() |
EM-85 | EM-91 | EM-97 |
EM-98 ![]() |
EM-120 | EM-184 |
EM-321 ![]() |
EM-337 | EM-423 | EM-447 | EM-448 | EM-456 | EM-650 |
EM-719 ![]() |
EM-844 | EM-888 | EM-963 | EM-993 | EM-1022 |
Summary Description
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**Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses." | AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Water flow regulation is a function of the storage and retention components of the water supply service (de Groot et al., 2002). The ability of a catchment to regulate flows is directly related to the volume of water that is retained or stored in the soil and underlying aquifers as moisture or groundwater; and the infiltration rate of water which replenishes the stored water (Kittredge, 1948; Farvolden, 1963). Groundwater contribution to surface runoff is the most direct measure of the water regulation function of a catchment. Data on the percentage contribution of groundwater to baseflows were obtained from DWAF (2005) per quaternary catchment and expressed as a percentage of total surface runoff, the range and hotspot being defined as areas with at least 10% and 30%, respectively (Colvin et al., 2007)." | ABSTRACT: "We describe a framework called Regional Hydrologic Modeling for Environmental Evaluation (RHyME2) for hydrologic modeling across scales. Rooted from hierarchy theory, RHyME2 acknowledges the rate-based hierarchical structure of hydrological systems. Operationally, hierarchical constraints are accounted for and explicitly described in models put together into RHyME2. We illustrate RHyME2with a two-module model to quantify annual nutrient loads in stream networks and watersheds at regional and subregional levels. High values of R2 (>0.95) and the Nash–Sutcliffe model efficiency coefficient (>0.85) and a systematic connection between the two modules show that the hierarchy theory-based RHyME2 framework can be used effectively for developing and connecting hydrologic models to analyze the dynamics of hydrologic systems." Two EMs will be entered in EPF-Library: 1. Regional scale module (Upper Mississippi River Basin) - this entry 2. Subregional scale module (St. Croix River Basin) | AUTHORS' DESCRIPTION: "AnnAGNPS is an advanced simulation model developed by the USDA-ARS and Natural Resource Conservation Services (NRCS) to help evaluate watershed response to agricultural management practices. It is a continuous simulation, daily time step, pollutant loading model designed to simulate water, sediment and chemical movement from agricultural watersheds.p. 198" | **Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** AUTHORS' DESCRIPTION: "PATCH (program to assist in tracking critical habitat), the SEPM used here, is designed for studying territorial vertebrates. It links the survival and fecundity of individual animals to geographic information system (GIS) data on mortality risk and habitat productivity at the scale of an individual or pack territory. Territories are allocated by intersecting the GIS data with an array of hexagonal cells. The different habitat types in the GIS maps are assigned weights based on the relative levels of fecundity and survival expected in those habitat classes. Base survival and reproductive rates, derived from published field studies, are then supplied to the model as a population projection matrix. The model scales these base matrix values using the mean of the habitat weights within each hexagon, with lower means translating into lower survival rates or reproductive output. Each individual in the population is tracked through a yearly cycle of survival, fecundity, and dispersal events. Environmental stochasticity is incorporated by drawing each year’s base population matrix from a randomized set of matrices whose elements were drawn from a beta (survival) or normal (fecundity) distribution. Adult organisms are classified as either territorial or floaters. The movement of territorial individuals is governed by a parameter for site fidelity, but floaters must always search for available breeding sites. As pack size increases, pack members in the model have a greater tendency to disperse and search for new available breeding sites. Movement decisions use a directed random walk that combines varying proportions of randomness, correlation, and attraction to higher-quality habitat (Schumaker 1998)." | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Habitat diversity” … The potential to deliver services is assumed to be influenced by land-use … and bioclimatic and landscape properties such as mountainous terrain, adjacency to coastal and wetland ecosystems, as well as adjacency to landscape protection zones." AUTHOR'S DESCRIPTION: "The analysis for the regulating service "Habitat Diversity" seeks to identify all the areas with potential to support biodiversity." | ABSTRACT: "Research on ecosystem services mapping and valuing has increased significantly in recent years. However, compared to provisioning and regulating services, cultural ecosystem services have not yet beenfully integrated into operational frameworks. One reason for this is that transdisciplinarity is required toaddress the issue, since by definition cultural services (encompassing physical, intellectual, spiritual inter-actions with biota) need to be analysed from multiple perspectives (i.e. ecological, social, behavioural).A second reason is the lack of data for large-scale assessments, as detailed surveys are a main sourceof information. Among cultural ecosystem services, assessment of outdoor recreation can be based ona large pool of literature developed mostly in social and medical science, and landscape and ecologystudies. This paper presents a methodology to include recreation in the conceptual framework for EUwide ecosystem assessments (Maes et al., 2013), which couples existing approaches for recreation man-agement at country level with behavioural data derived from surveys and population distribution data.The proposed framework is based on three components: the ecosystem function (recreation potential),the adaptation of the Recreation Opportunity Spectrum framework to characterise the ecosystem serviceand the distribution of potential demand in the EU." | ABSTRACT: "We present an integrative conceptual framework to estimate the provision of soil erosion prevention (SEP) by combining the structural impact of soil erosion and the social–ecological processes that allow for its mitigation. The framework was tested and illustrated in the Portel municipality in Southern Portugal, a Mediterranean silvo-pastoral system that is prone to desertification and soil degradation. The results show a clear difference in the spatial and temporal distribution of the capacity for ecosystem service provision and the actual ecosystem service provision." AUTHOR'S DESCRIPTION: "To begin assessing the contribution of SEP we need to identify the structural impact of soil erosion, that is, the erosion that would occur when vegetation is absent and therefore no ES is provided. It determines the potential soil erosion in a given place and time and is related to rainfall erosivity (that is, the erosive potential of rainfall), soil erodibility (as a characteristic of the soil type) and local topography. Although external drivers can have an effect on these variables (for example, climate change), they are less prone to be changed directly by human action. The actual ES provision reduces the total amount of structural impact, and we define the remaining impact as the ES mitigated impact. We can then define the capacity for ES provision as a key component to determine the fraction of the structural impact that is mitigated…Following the conceptual outline, we will estimate the SEP provided by vegetation cover using an adaptation of the Universal Soil Loss Equation (USLE)." | ABSTRACT: "The development of a mathematical model for predicting rate of fire spread and intensity applicable to a wide range of wildland fuels is presented from the conceptual stage through evaluation and demonstration of results to hypothetical fuel models. The model was developed for and is now being used as a basis for appraising fire spread and intensity in the National Fire Danger Rating System. The initial work was done using fuel arrays composed of uniform size particles. Three fuel sizes were tested over a wide range of bulk densities. These were 0.026-inch-square cut excelsior, 114-inch sticks, and 112-inch sticks. The problem of mixed fuel sizes was then resolved by weighting the various particle sizes that compose actual fuel arrays by either surface area or loading, depending upon the feature of the fire being predicted. The model is complete in the sense that no prior knowledge of a fuel's burning characteristics is required. All that is necessary are inputs describing the physical and chemical makeup of the fuel and the environmental conditions in which it is expected to burn. Inputs include fuel loading, fuel depth, fuel particle surface-area-to-volume ratio, fuel particle heat content, fuel particle moisture and mineral content, and the moisture content at which extinction can be expected. Environmental inputs are mean wind velocity and slope of terrain. For heterogeneous mixtures, the fuel properties are entered for each particle size. The model as originally conceived was for dead fuels in a uniform stratum contiguous to the ground, such as litter or grass. It has been found to be useful, however, for fuels ranging from pine needle litter to heavy logging slash and for California brush fields." **FARSITE4 will no longer be supported or available for download or further supported. FlamMap6 now includes FARSITE.** | AUTHOR'S DESCRIPTION: "Air pollutant removal, particularly of large dust particles relevant to asthma, was identified as an ecosystem service contributing to the stakeholder objective to improve air quality…Rates of air pollutant removal depend on the downward flux of particles intercepted by the tree canopy…Because atmospheric pollutant concentration can vary widely across space and time, we standardized across watersheds by calculating the removal rate per unit concentration of pollutant, assuming a pollutant concentration of 1 g m^-3. Specifically, the removal rate was calculated per unit concentration of particulate matter greater than…PM<sub>10, applying a typical deposition velocity of 1.25 cm s^-1…" | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion, storm damage, or coastal inundation during extreme events (UNEP-WCMC (United Nations Environment Programme, World Conservation Monitoring Centre), 2006; WRI (World Resources Institute), 2009), but is often quantified as wave energy attenuation, an intermediate service that contributes to shoreline protection by reducing rates of erosion or coastal inundation (Principeet al., 2012). From earlier models Gourlay (1996, 1997), Sheppard et al. (2005) developed a tool that can be used to estimate the percent attenuation in wave energy or wave height due to the presence of the reef. The decay in wave heights over the reef is described by dHrs/dx = -(fw/3π) (Hrs2/ (ηr+hr+ηw)2 where Hrs is significant wave height, fw is friction over the reef top, hr is water depth over the reef flat, and ηw is meteorological surge. Wave set-up on the reef-flat, ηr…" | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion, storm damage, or coastal inundation during extreme events (UNEP-WCMC (United Nations Environment Programme, World Conservation Monitoring Centre), 2006; WRI (World Resources Institute), 2009), but is often quantified as wave energy attenuation, an intermediate service that contributes to shoreline protection by reducing rates of erosion or coastal inundation (Principeet al., 2012)...The energy (attenuation) in a moving wave (E) can then be calculated by E = 1/8ρgH^2 where ρ is the density of seawater (1025 kg m^-3) and H is wave height (attenuation)." | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…In lieu of surveys of diver opinion, recreational opportunities can also be estimated by actual field data of coral condition at preferred dive sites. A few studies have directly examined links between coral condition and production of recreational opportunities through field monitoring in an attempt to validate perceptions of recreational quality (Pendleton, 1994; Williams and Polunin, 2002; Leeworthy et al., 2004; Leujakand Ormond, 2007; Uyarraetal., 2009). Uyarraetal. (2009) used surveys to determine reef attributes related to diver perceptions of most and least favorite dive sites. Field data was used to narrow down the suite of potential preferred attributes to those that reflected actual site condition. We combined these attributes to form an index of dive site favorability: Dive site favorability = ΣipiRi where pi is the proportion of respondents indicating each attribute i that affected dive enjoyment positively. Ri is the mean relative magnitude of measured variables used to quantify each descriptive attribute i, including ‘fish abundance’ (pi=0.803), quantified by number of fish schools and fish species richness, and | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Sedge Wren (Cistothorus platensis)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: SEWR density = 1-1/1+e^(-0.8015652 + 0.08500569 * grass400) *e^(-0.7982511 + 0.0285891 * bbspath + 0.0105094 *grass400) | AUTHOR'S DESCRIPTION: "Restoring ecosystem services at scale requires executing conservation programs in a way that is resource and cost efficient as well as ecologically effective…Seed mix design is one of the largest determinants of project cost and ecological outcomes for prairie reconstructions. In particular, grass-to-forb seeding ratio affects cost since forb seed can be much more expensive relative to grass species (Prairie Moon Nursery 2012). Even for seed mixes with the same overall seeding rates, a mix with a low grass-to-forb seeding ratio is considerably more expensive than one with a high grass-to-forb ratio. Seeding rates for different plant functional groups that are too high or low may also adversely affect ecological outcomes…First-year management may also play a role in cost-effective prairie reconstruction. Post-agricultural sites where restoration typically occurs are often quickly dominated by fast-growing annual weeds by the time sown prairie seeds begin germinating (Smith et al. 2010)… Williams and others (2007) showed that prairie seedlings sown into established warm-season grasses were reliant on high light conditions created by frequently mowing tall vegetation in order to survive in subsequent years…Our objective was to compare native plant establishment and cost effectiveness with and without first-year mowing for three different seed mixes that differed in grass to forb ratio and soil type customization. With knowledge of plant establishment, cost effectiveness, and mowing management outcomes, conservation practitioners will be better equipped to restore prairie efficiently and successfully." | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. " | ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context." | ABSTRACT: The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehen- sive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively- related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. AUTHOR'S DESCRIPTION: For each species, we developed multiple regression models for the entire study area and for each of the seven ecological regions separately. We included only those routes that met quality standards for both bird abundance and land use data, and this left a total of 636 useable routes. The number of routes within individual ecological regions ranged from a low of 55 (central hardwoods) to a high of 154 (Appalachian Mountains). Using our estimates of bird abundance as response variables and landscape variables as explanatory variables, we used a stepwise selection process (retaining only explanatory variables that satisfied α < 0.05) to build models for each of the seven ecological regions and for the study region as a whole. | ABSTRACT: "Coho salmon (Oncorhynchus kisutch) are highly sensitive to 6PPD-Quinone (6PPD-Q). Details of the hydrological and biogeochemical processes controlling spatial and temporal dynamics of 6PPD-Q fate and transport from points of deposition to receiving waters (e.g., streams, estuaries) are poorly understood. To understand the fate and transport of 6PPD and mechanisms leading to salmon mortality Visualizing Ecosystem Land Management Assessments (VELMA), an ecohydrological model developed by US Environmental Protection Agency (EPA), was enhanced to better understand and inform stormwater management planning by municipal, state, and federal partners seeking to reduce stormwater contaminant loads in urban streams draining to the Puget Sound National Estuary. This work focuses on the 5.5 km2 Longfellow Creek upper watershed (Seattle, Washington, United States), which has long exhibited high rates of acute urban runoff mortality syndrome in coho salmon. We present VELMA model results to elucidate these processes for the Longfellow Creek watershed across multiple scales–from 5-m grid cells to the entire watershed. Our results highlight hydrological and biogeochemical controls on 6PPD-Q flow paths, and hotspots within the watershed and its stormwater infrastructure, that ultimately impact contaminant transport to Longfellow Creek and Puget Sound. Simulated daily average 6PPD-Q and available observed 6PPD-Q peak in-stream grab sample concentrations (ng/L) corresponds within plus or minus 10 ng/L. Most importantly, VELMA’s high-resolution spatial and temporal analysis of 6PPD-Q hotspots provides a tool for prioritizing the locations, amounts, and types of green infrastructure that can most effectively reduce 6PPD-Q stream concentrations to levels protective of coho salmon and other aquatic species. " | ABSTRACT: "With support from NASA's Modeling and Analysis Program, we have recently developed the NASA Unified-Weather Research and Forecasting model (NU-WRF). NU-WRF is an observation-driven integrated modeling system that represents aerosol, cloud, precipitation and land processes at satelliteresolved scales. “Satellite-resolved” scales (roughly 1e25 km), bridge the continuum between local (microscale), regional (mesoscale) and global (synoptic) processes. NU-WRF is a superset of the National Center for Atmospheric Research (NCAR) Advanced Research WRF (ARW) dynamical core model, achieved by fully integrating the GSFC Land Information System (LIS, already coupled to WRF), the WRF/Chem enabled version of the Goddard Chemistry Aerosols Radiation Transport (GOCART) model, the Goddard Satellite Data Simulation Unit (G-SDSU), and custom boundary/initial condition preprocessors into a single software release, with source code available by agreement with NASA/GSFC. Full coupling between aerosol, cloud, precipitation and land processes is critical for predicting local and regional water and energy cycles " |
Specific Policy or Decision Context Cited
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Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…" | None identified | Not reported | Not reported | AUTHOR DESCRIPTION: "Comprehensive habitat and viability assessments. . . [more rigoursly defined] can clarify debate of goals for recovery of large carnivores"; Endangered Species Act and related litigation | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | Seed mix design and management practices for native plant restoration | None reported | None identified | Food Security Act of 1985 | Not reported | Not Applicable |
Biophysical Context
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No additional description provided | Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | No additional description provided | Upper Mississipi River basin, elevation 142-194m, | Great Plains to Pacific Coast, northern Rocky Mountains, Pacific Northwest | No additional description provided | No additional description provided | Open savannah-like forest of cork (Quercus suber) and holm (Quercus ilex) oaks, with trees of different ages randomly dispersed in changing densities, and pastures in the under cover. The pastures are mostly natural in a mosaic with patches of shrubs, which differ in size and the distribution depends mainly on the grazing intensity. Shallow, poor soils are prone to erosion, especially in areas with high grazing pressure. | Not applicable | No additional description provided | No additional description provided | No additional description provided | No additional description provided | Prairie pothole region of north-central Iowa | The soils underlying the study site are primarily poorly drained Clyde clay loams, with a minor component of somewhat poorly drained Floyd loams in the northwest (NRCS 2016). Topographically, the study site is level, and slopes do not exceed 5% grade. Land use prior to this experiment was agricultural, with corn and soybeans consistently grown in rotation at the site. | Conservation Reserve Program lands left to go fallow | Waterfront districts on south Lake Michigan and south lake Erie | Bird Conservation Regions ranging from Central to eastern United States and from the Gulf of Mexico to the Great Lakes. | 6PPD deposition from vehicle tire wear particles. | Atmospheric variables |
EM Scenario Drivers
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Five scenarios that include urban growth boundaries and various combinations of unconstrainted development, fish conservation, agriculture and forest reserves. ?Comment:Additional alternatives included adding agricultural and forest reserves, and adding or removing urban growth boundaries to the three main scenarios. |
No scenarios presented | No scenarios presented | Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | Population growth, road development (density) on public vs private land | No scenarios presented | No scenarios presented | Different land management practices as represented by the comparison of different grazing intensities (i.e., livestock densities) in the whole study area and in three Civil Parishes within the study area | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | N/A | N/A | Separate models created for each Bird Conservation Region, including different land use, agriculture, and CRP variable values. | N/A | Not applicable |
EM ID
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EM-12 ![]() |
EM-85 | EM-91 | EM-97 |
EM-98 ![]() |
EM-120 | EM-184 |
EM-321 ![]() |
EM-337 | EM-423 | EM-447 | EM-448 | EM-456 | EM-650 |
EM-719 ![]() |
EM-844 | EM-888 | EM-963 | EM-993 | EM-1022 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application (multiple runs exist) | Method + Application | Method Only |
New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | Application of existing model |
Application of existing model ?Comment:Models developed by Quamen (2007). |
New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
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EM-12 ![]() |
EM-85 | EM-91 | EM-97 |
EM-98 ![]() |
EM-120 | EM-184 |
EM-321 ![]() |
EM-337 | EM-423 | EM-447 | EM-448 | EM-456 | EM-650 |
EM-719 ![]() |
EM-844 | EM-888 | EM-963 | EM-993 | EM-1022 |
EM Temporal Extent
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1990-2050 | Not reported | 1987-1997 | 1980-2006 | 2000-2025 | 2000 | Not reported | January to December 2003 | Not applicable | 2013 | 2006-2007, 2010 | 2006-2007, 2010 | 2006-2007, 2010 | 1992-2007 | 2015-2017 | 2008 | 2022 | 1995-1999 | 9/2020-6/2021 | Not applicable |
EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | Not applicable | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | Not applicable |
EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable |
EM Time Continuity
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discrete | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | discrete | Not applicable |
EM Temporal Grain Size Value
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2 | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | 1 | Not applicable |
EM Temporal Grain Size Unit
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Year | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Month | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable | Day | Not applicable |
EM ID
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EM-12 ![]() |
EM-85 | EM-91 | EM-97 |
EM-98 ![]() |
EM-120 | EM-184 |
EM-321 ![]() |
EM-337 | EM-423 | EM-447 | EM-448 | EM-456 | EM-650 |
EM-719 ![]() |
EM-844 | EM-888 | EM-963 | EM-993 | EM-1022 |
Bounding Type
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Geopolitical | Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical | Geopolitical | Geopolitical | Not applicable | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Other | Physiographic or ecological | Geopolitical | Physiographic or ecological | Watershed/Catchment/HUC | Not applicable |
Spatial Extent Name
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Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | South Africa | Upper Mississippi River basin; St. Croix River Watershed | East Fork Kaskaskia River watershed basin | Western United States | The EU-25 plus Switzerland and Norway | European Union countries | Portel municipality | Not applicable | Guanica Bay watershed | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | CREP (Conservation Reserve Enhancement Program) wetland sites | Iowa State University Northeast Research and Demonstration Farm | Piedmont Ecoregion | Great Lakes waterfront | Bird Conservation Regions comprising the northern bobwhite breeding range. | Longfellow creek | Not applicable |
Spatial Extent Area (Magnitude)
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10-100 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | Not applicable | 1000-10,000 km^2. | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 1-10 km^2 | <1 ha | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | >1,000,000 km^2 | 1-10 km^2 | Not applicable |
EM ID
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EM-12 ![]() |
EM-85 | EM-91 | EM-97 |
EM-98 ![]() |
EM-120 | EM-184 |
EM-321 ![]() |
EM-337 | EM-423 | EM-447 | EM-448 | EM-456 | EM-650 |
EM-719 ![]() |
EM-844 | EM-888 | EM-963 | EM-993 | EM-1022 |
EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | Not applicable |
spatially distributed (in at least some cases) ?Comment:pp. 14 - "Most ecosystem services were mapped at the same resolution as the LULC data (30 x 30 m^2)." I assumed that, unless otherwise specified, calculations were carried out on a 30 x 30 m^2 pixel. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | other or unclear (comment) |
Spatial Grain Type
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area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | NHDplus v1 | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | Not applicable | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable |
Spatial Grain Size
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varies | Distributed by catchments with average size of 65,000 ha | NHDplus v1 | 1 km^2 | 504 km^2 | 1 km x 1 km | 100 m x 100 m | 250 m x 250 m | Not applicable | 30 m x 30 m | 10 m x 10 m | 10 m x 10 m | 10 m x 10 m | multiple, individual, irregular shaped sites | 20 ft x 28 ft | Not applicable | Not applicable | 1962 km^2 | Not applicable | Not applicable |
EM ID
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EM-12 ![]() |
EM-85 | EM-91 | EM-97 |
EM-98 ![]() |
EM-120 | EM-184 |
EM-321 ![]() |
EM-337 | EM-423 | EM-447 | EM-448 | EM-456 | EM-650 |
EM-719 ![]() |
EM-844 | EM-888 | EM-963 | EM-993 | EM-1022 |
EM Computational Approach
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Numeric | Analytic | Numeric | Numeric | Numeric | Logic- or rule-based | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic |
EM Determinism
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stochastic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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Comment:Agent based modeling results in response indices. |
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EM ID
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EM-12 ![]() |
EM-85 | EM-91 | EM-97 |
EM-98 ![]() |
EM-120 | EM-184 |
EM-321 ![]() |
EM-337 | EM-423 | EM-447 | EM-448 | EM-456 | EM-650 |
EM-719 ![]() |
EM-844 | EM-888 | EM-963 | EM-993 | EM-1022 |
Model Calibration Reported?
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Unclear | No | Yes | No | Unclear | No | No | No | Not applicable | Yes | Yes | Yes | Yes | Unclear | Not applicable | Yes | No |
Unclear ?Comment:Does accounting for autocorrelation count as validation? |
Yes | Not applicable |
Model Goodness of Fit Reported?
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No | No | Yes | No | No | No | No | No | Not applicable | No | No | No | No | No | Not applicable | No | No | Not applicable | No | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None |
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None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None |
Model Operational Validation Reported?
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No | No | No | Yes | No | Yes | No | No | No | No | Yes | Yes | Yes | Unclear | No | No | No | No | Yes | Not applicable |
Model Uncertainty Analysis Reported?
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No | No | No | Yes | No | No | No | No | Not applicable | No | No | No | No | No | Not applicable | No | No | No | Unclear | Not applicable |
Model Sensitivity Analysis Reported?
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No ?Comment:Sensitivity analysis performed for agent values only. |
No |
No ?Comment:Some model coefficients serve, by their magnitude, to indicate the proportional impact on the final result of variation in the parameters they modify. |
Unclear |
Yes ?Comment:No results reported. Just a general statement was made about PATCH sensitivity and that demographic parameters are more sensitive that variation in other parameters such as dispersadistance . Reference made to another publication Carroll et al. 2003. Use of population viability analysis and reserve slelection algorithms in regional conservation plans. Ecol. App. 13:1773-1789. |
No | No | No | Not applicable | No | No | No | No | No | Not applicable | Yes | Yes | No | Unclear | Not applicable |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-12 ![]() |
EM-85 | EM-91 | EM-97 |
EM-98 ![]() |
EM-120 | EM-184 |
EM-321 ![]() |
EM-337 | EM-423 | EM-447 | EM-448 | EM-456 | EM-650 |
EM-719 ![]() |
EM-844 | EM-888 | EM-963 | EM-993 | EM-1022 |
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None | None | None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-12 ![]() |
EM-85 | EM-91 | EM-97 |
EM-98 ![]() |
EM-120 | EM-184 |
EM-321 ![]() |
EM-337 | EM-423 | EM-447 | EM-448 | EM-456 | EM-650 |
EM-719 ![]() |
EM-844 | EM-888 | EM-963 | EM-993 | EM-1022 |
None | None | None | None | None | None | None | None | None |
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None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-12 ![]() |
EM-85 | EM-91 | EM-97 |
EM-98 ![]() |
EM-120 | EM-184 |
EM-321 ![]() |
EM-337 | EM-423 | EM-447 | EM-448 | EM-456 | EM-650 |
EM-719 ![]() |
EM-844 | EM-888 | EM-963 | EM-993 | EM-1022 |
Centroid Latitude
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44.11 | -30 | 42.5 | 38.69 | 39.88 | 50.53 | 48.2 | 38.3 | -9999 | 17.96 | 17.73 | 17.73 | 17.73 | 42.62 | 42.93 | 36.23 | 42.26 | 36.53 | 47.55 | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
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-123.09 | 25 | -90.63 | -89.1 | -113.81 | 7.6 | 16.35 | -7.7 | -9999 | -67.04 | -64.77 | -64.77 | -64.77 | -93.84 | -92.57 | -81.9 | -87.84 | -88.45 | 122.37 | Not applicable |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | NAD83 | None provided | Not applicable |
Centroid Coordinates Status
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Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Provided | Not applicable |
EM ID
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EM-12 ![]() |
EM-85 | EM-91 | EM-97 |
EM-98 ![]() |
EM-120 | EM-184 |
EM-321 ![]() |
EM-337 | EM-423 | EM-447 | EM-448 | EM-456 | EM-650 |
EM-719 ![]() |
EM-844 | EM-888 | EM-963 | EM-993 | EM-1022 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere | Agroecosystems | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Scrubland/Shrubland | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Open Ocean and Seas | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Atmosphere | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Inland Wetlands | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Grasslands | Terrestrial Environment (sub-classes not fully specified) |
Terrestrial Environment (sub-classes not fully specified) ?Comment:Is there a way to choose more than one? |
Rivers and Streams | Atmosphere |
Specific Environment Type
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Agricultural-urban interface at river junction | Not reported | None | Row crop agriculture in Kaskaskia river basin | Not reported | Not applicable | Not applicable | Silvo-pastoral system | Not applicable | Multiple environmental types present | Coral reefs | Coral reefs | Coral reefs | Grassland buffering inland wetlands set in agricultural land | Research farm in historic grassland | grasslands | Lake Michigan waterfront | A mixture of developed and natural environments including cultivated and non-cultivated cropland, pastures, roads / railways, and urban areas as well as grasslands, forest, and freshwater habitats spanning the central to eastern United States. | small stream | Regional wealther |
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class | Ecosystem | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-12 ![]() |
EM-85 | EM-91 | EM-97 |
EM-98 ![]() |
EM-120 | EM-184 |
EM-321 ![]() |
EM-337 | EM-423 | EM-447 | EM-448 | EM-456 | EM-650 |
EM-719 ![]() |
EM-844 | EM-888 | EM-963 | EM-993 | EM-1022 |
EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Species | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Community | Not applicable | Guild or Assemblage | Species | Community | Species | Not applicable | Species | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-12 ![]() |
EM-85 | EM-91 | EM-97 |
EM-98 ![]() |
EM-120 | EM-184 |
EM-321 ![]() |
EM-337 | EM-423 | EM-447 | EM-448 | EM-456 | EM-650 |
EM-719 ![]() |
EM-844 | EM-888 | EM-963 | EM-993 | EM-1022 |
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None Available | None Available | None Available |
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None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available |
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None Available |
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None Available |
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None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-12 ![]() |
EM-85 | EM-91 | EM-97 |
EM-98 ![]() |
EM-120 | EM-184 |
EM-321 ![]() |
EM-337 | EM-423 | EM-447 | EM-448 | EM-456 | EM-650 |
EM-719 ![]() |
EM-844 | EM-888 | EM-963 | EM-993 | EM-1022 |
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None |
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None | None |
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None |
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None |
<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-12 ![]() |
EM-85 | EM-91 | EM-97 |
EM-98 ![]() |
EM-120 | EM-184 |
EM-321 ![]() |
EM-337 | EM-423 | EM-447 | EM-448 | EM-456 | EM-650 |
EM-719 ![]() |
EM-844 | EM-888 | EM-963 | EM-993 | EM-1022 |
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None | None |
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None |
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None | None | None | None |
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None |
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Comment:Model identifies toxicant concentrations relative to the known LC50 for coho juveniles which is 95ng/L (Spromber and Scholz, 2011; |
None |